Warning Spikewrap is not sufficiently tested to be used in analysis. This release is only for testing. Do not use for your final analyses.
Warning Limitations
- works only on SpikeGLX recordings with 1 gate, 1 trigger, 1 probe (per run, e.g. g0, t0, imec0)
- requires standard input folder format
- only run one subject / run at a time
- has limited preprocessing options (
tshift,bandpass_filter,common median reference)- no options to remove potentially large intermediate files
- installation / running on HPC is a bit clunky. In future this can be simplified with SLURM jobs organised under the hood and setting up a HPC module.
- untested!
- The documentation is currently outdated.
- preprocess SpikeGLX data (
tshift,bandpass_filter,common median reference) - spike sorting (
kilosort2,kilosort2_5,kilosort3) - quality check measures on the sorting results
Sorting requires a NVIDIA GPU and so is currently only available using the SWC's High-Performance Computer (HPC). However, local installation is useful for visualising the preprocessing steps prior to running the full pipeline (see 'Visualisation' below).
To install locally, clone the repository to your local machine using git.
git clone [email protected]:neuroinformatics-unit/spikewrap.git
Change directory to the repo and install using
pip install -e .
or, to also install developer dependencies
pip install -e .[dev]
or if using the zsh shell
pip install -e ".[dev]"
After installation, the module can be imported with import spikewrap.
Currently, sorting is required to run on the SWC HPC with access to /ceph/neuroinformatics.
To connect and run on the HPC (e.g. from Windows, macOS or Linux terminal):
ssh hpc-gw1
The first time using, it is necessary to steup and install spikewrap. It is strongly recommended to make a new conda environment on the HPC, before installing spikewrap.
module load miniconda
conda create --name spikewrap python=3.10
conda activate spikewrap
and install spikewrap and it's dependencies:
mkdir ~/git-repos
cd ~/git-repos
git clone https://github.com/JoeZiminski/spikewrap.git
cd spikewrap
pip install -e .
Before running, it is necessary to request use of a GPU node on the HPC to run spike sorting with KiloSort. To run preprocessing and spike sorting, create a script using the API or call from the command line interface (instructions below).
srun -p gpu --gres=gpu:1 -n 8 --mem=40GB --pty bash -i
module load cuda
module load miniconda
conda activate spikewrap
python my_pipeline_script.py
Spikewrap (currently) expects input data to be stored in a rawdata folder. A subject (e.g. mouse) data should be stored in the rawdata folder and contain SpikeGLX output format (example below). Currently, only recordings with 1 gate, 1 trigger and 1 probe are supported (i.e. index 0 for all gate, trigger probe, g0, t0 and imec0).
└── rawdata/
└── 1110925/
└── 1110925_test_shank1_g0/
└── 1110925_test_shank1_g0_imec0/
├── 1110925_test_shank1_g0_t0.imec0.ap.bin
└── 1110925_test_shank1_g0_t0.imec0.ap.meta
Example code to analyse this data in this format is below:
from spikewrap.pipeline.full_pipeline import run_full_pipeline
base_path = "/ceph/neuroinformatics/neuroinformatics/scratch/ece_ephys_learning"
if __name__ == "__main__":
run_full_pipeline(
base_path=base_path,
sub_name="1110925",
run_name="1110925_test_shank1",
config_name="test",
sorter="kilosort2_5",
)
base_path is the path containing the required rawdata folder.
sub_name is the subject to run, and run_name is the SpikeGLX run name to run.
configs_name contains the name of the preprocessing / sorting settings to use (see below)
sorter is the name of the sorter to use (currently supported is kilosort2, kilosort2_5 and kilosort3)
Note run_full_pipline must be run in the if __name__ == "__main__" block as it uses the multiprocessing module.
Output of spike sorting will be in a derivatives folder at the same level as the rawdata. The subfolder organisation of derivatives will match rawdata.
Output are the saved preprocessed data, spike sorting results as well as a list of quality check measures. For example, the full output of a sorting run with the input data as above is:
├── rawdata/
│ └── ...
└── derivatives/
└── 1110925/
└── 1110925_test_shank1_g0 /
└── 1110925_test_shank1_g0_imec0/
├── preprocessed/
│ ├── data_class.pkl
│ └── si_recording
├── kilosort2_5-sorting/
├── in_container_sorting/
├── sorter_output/
├── waveforms/
│ └── <spikeinterface waveforms output>
├── quality_metrics.csv
├── spikeinterface_log.json
├── spikeinterface_params.json
└── spikeinterface_recording.json
preprocessed:
- Binary-format spikeinterface recording from the final preprocessing step (
si_recording) 2)data_class.pklspikewrap internal use.
-sorting output (e.g. kilosort2_5-sorting, multiple sorters can be run):
-
in_container_sorting: stored options used to run the sorter
-
sorter_output: the full output of the sorter (e.g. kilosort .npy files)
-
waveforms: spikeinterface waveforms output containing AP waveforms for detected spikes
-
quality_metrics.csv: output of spikeinterface quality check measures
Currently supported are multiplexing correction or tshift (termed phase shift here), common median referencing (CMR) (termed common_reference here) and bandpass filtering (bandpass_filter). These options provide an interface to SpikeInterface preprocessing options, more will be added soon.
Preprocessing options are set in yaml configuration files stored in sbi_ephys/sbi_ephys/configs/. A default pipeline is stored in test.yaml.
Custom preprocessing configuration files may be passed to the config_name argument, by passing the full path to the .yaml configuration file. For example:
'preprocessing':
'1':
- phase_shift
- {}
'2':
- bandpass_filter
- freq_min: 300
freq_max: 6000
'3':
- common_reference
- operator: median
reference: global
'sorting':
'kilosort3':
'car': False
'freq_min': 300
Configuration files are structured as a dictionary where keys indicate the order to run preprocessing The values hold a list in which the first element is the name of the preprocessing step to run, and the second element a dictionary containing kwargs passed to the spikeinterface function.
Visualising preprocesing output can be run locally to inspect output of preprocessing routines. To visualise preprocessing outputs:
from spikewrap.pipeline.preprocess import preprocess
from spikewrap.pipeline.visualise import visualise
base_path = "/ceph/neuroinformatics/neuroinformatics/scratch/ece_ephys_learning"
sub_name = "1110925"
run_name = "1110925_test_shank1"
data = preprocess(base_path=base_path, sub_name=sub_name, run_name=run_name)
visualise(
data,
steps="all",
mode="map",
as_subplot=True,
channel_idx_to_show=np.arange(10, 50),
show_channel_ids=False,
time_range=(1, 2),
)
This will display a plot showing data from all preprocessing steps, displaying channels with idx 10 - 50, over time period 1-2. Note this requires a GUI (i.e. not run on the HPC terminal) and is best run locally.
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